


import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()






def 					UpperCamelCase   (      __magic_name__	:				Any       ,       __magic_name__	:				Optional[int]       ,       __magic_name__	:				Dict       ,       __magic_name__	:				Tuple       ,       __magic_name__	:				str	)  ->    Tuple:
		"""simple docstring"""




		lowercase__	     =						TapasConfig.from_json_file(lowerCAmelCase__	)
		# set absolute/relative position embeddings parameter
		lowercase__	     =						reset_position_index_per_cell

		# set remaining parameters of TapasConfig as well as the model based on the task
		if task == "SQA":
				lowercase__	     =						TapasForQuestionAnswering(config=lowerCAmelCase__	)
		elif task == "WTQ":
				# run_task_main.py hparams
				lowercase__	     =						4
				lowercase__	     =						True
				# hparam_utils.py hparams
				lowercase__	     =						0.6_6_4_6_9_4
				lowercase__	     =						0.2_0_7_9_5_1
				lowercase__	     =						0.1_2_1_1_9_4
				lowercase__	     =						True
				lowercase__	     =						True
				lowercase__	     =						False
				lowercase__	     =						0.0_3_5_2_5_1_3

				lowercase__	     =						TapasForQuestionAnswering(config=lowerCAmelCase__	)
		elif task == "WIKISQL_SUPERVISED":
				# run_task_main.py hparams
				lowercase__	     =						4
				lowercase__	     =						False
				# hparam_utils.py hparams
				lowercase__	     =						3_6.4_5_1_9
				lowercase__	     =						0.9_0_3_4_2_1
				lowercase__	     =						2_2_2.0_8_8
				lowercase__	     =						True
				lowercase__	     =						True
				lowercase__	     =						True
				lowercase__	     =						0.7_6_3_1_4_1

				lowercase__	     =						TapasForQuestionAnswering(config=lowerCAmelCase__	)
		elif task == "TABFACT":
				lowercase__	     =						TapasForSequenceClassification(config=lowerCAmelCase__	)
		elif task == "MLM":
				lowercase__	     =						TapasForMaskedLM(config=lowerCAmelCase__	)
		elif task == "INTERMEDIATE_PRETRAINING":
				lowercase__	     =						TapasModel(config=lowerCAmelCase__	)
		else:
				raise ValueError(f'''Task {task} not supported.'''	)

		print(f'''Building PyTorch model from configuration: {config}'''	)
		# Load weights from tf checkpoint
		load_tf_weights_in_tapas(lowerCAmelCase__       ,       lowerCAmelCase__       ,       lowerCAmelCase__	)

		# Save pytorch-model (weights and configuration)
		print(f'''Save PyTorch model to {pytorch_dump_path}'''	)
		model.save_pretrained(lowerCAmelCase__	)

		# Save tokenizer files
		print(f'''Save tokenizer files to {pytorch_dump_path}'''	)
		lowercase__	     =						TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + """vocab.txt"""       ,       model_max_length=512	)
		tokenizer.save_pretrained(lowerCAmelCase__	)

		print("""Used relative position embeddings:"""       ,       model.config.reset_position_index_per_cell	)


if __name__ == "__main__":
	A			:						Optional[Any]        =  argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--task', default='SQA', type=str, help='Model task for which to convert a checkpoint. Defaults to SQA.'
	)
	parser.add_argument(
	    '--reset_position_index_per_cell',
	    default=False,
	    action='store_true',
	    help='Whether to use relative position embeddings or not. Defaults to True.',
	)
	parser.add_argument(
	    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
	)
	parser.add_argument(
	    '--tapas_config_file',
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        'The config json file corresponding to the pre-trained TAPAS model. \n'
	        'This specifies the model architecture.'
	    ),
	)
	parser.add_argument(
	    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)
	A			:						Optional[int]        =  parser.parse_args()
	convert_tf_checkpoint_to_pytorch(
	    args.task,
	    args.reset_position_index_per_cell,
	    args.tf_checkpoint_path,
	    args.tapas_config_file,
	    args.pytorch_dump_path,
	)






#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes        # name
# #SBATCH --nodes=2                    # nodes
# #SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10           # number of cores per tasks
# #SBATCH --gres=gpu:4                 # number of gpus
# #SBATCH --time 0:05:00               # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out           # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#

import fcntl
import os
import socket

import torch
import torch.distributed as dist







def  lowerCAmelCase_	(*lowerCAmelCase__:		Dict  ):





			"""simple docstring"""


			with open(lowerCAmelCase__		,						"""r"""  ) as fh:
						fcntl.flock(lowerCAmelCase__		,						fcntl.LOCK_EX  )
						try:
									print(*lowerCAmelCase__  )
						finally:
									fcntl.flock(lowerCAmelCase__		,						fcntl.LOCK_UN  )


a      :  Tuple			     =			int(os.environ['LOCAL_RANK'])
torch.cuda.set_device(local_rank)
a      :  Union[str, Any]			     =			torch.device('cuda', local_rank)
a      :  Any			     =			socket.gethostname()

a      :  Optional[Any]			     =			F'''[{hostname}-{local_rank}]'''

try:
	# test distributed
	dist.init_process_group('nccl')
	dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
	dist.barrier()

	# test cuda is available and can allocate memory
	torch.cuda.is_available()
	torch.ones(1).cuda(local_rank)

	# global rank
	a      :  Optional[Any]			     =			dist.get_rank()
	a      :  List[Any]			     =			dist.get_world_size()

	printflock(F'''{gpu} is OK (global rank: {rank}/{world_size})''')

	dist.barrier()
	if rank == 0:
		printflock(F'''pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}''')

except Exception:
	printflock(F'''{gpu} is broken''')
	raise






